# Quantifying the causal effect of speed cameras on road traffic accidents via an approximate Bayesian doubly robust estimator

## TL;DR

This paper introduces an approximate Bayesian doubly-robust method to estimate the causal impact of speed cameras on traffic accidents, addressing previous methodological limitations and providing evidence of safety benefits in England.

## Contribution

It develops a novel Bayesian DR approach for causal inference, combining propensity scores and outcome models, to evaluate transport safety interventions.

## Key findings

- Speed cameras reduce collisions by approximately 15%.
- The method provides a statistically robust estimate of causal effects.
- Results support the effectiveness of speed cameras in improving road safety.

## Abstract

This paper quantifies the effect of speed cameras on road traffic collisions using an approximate Bayesian doubly-robust (DR) causal inference estimation method. Previous empirical work on this topic, which shows a diverse range of estimated effects, is based largely on outcome regression (OR) models using the Empirical Bayes approach or on simple before and after comparisons. Issues of causality and confounding have received little formal attention. A causal DR approach combines propensity score (PS) and OR models to give an average treatment effect (ATE) estimator that is consistent and asymptotically normal under correct specification of either of the two component models. We develop this approach within a novel approximate Bayesian framework to derive posterior predictive distributions for the ATE of speed cameras on road traffic collisions. Our results for England indicate significant reductions in the number of collisions at speed cameras sites (mean ATE = -15%). Our proposed method offers a promising approach for evaluation of transport safety interventions.